Computer Vision: Models, Learning, and Inference

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Dr Simon J. D. PrinceCambridge University Press, 6/18/2012EAN 9781107011793, ISBN10: 1107011795Hardcover, 598 pages, 25.3 x 17.7 x 2.8 cmLanguage: EnglishThis modern treatment of c omputer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. • Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry • A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking • More than 70 algorithms are described in sufficient detail to implement • More than 350 full-color illustrations amplify the text • The treatment is self-contained, including all of the background mathematics • Additional resources at www.computervisionmodels.comPart I. Probability1. Introduction to probability2. Common probability distributions3. Fitting probability models4. The normal distributionPart II. Machine Learning for Machine Vision5. Learning and inference in vision6. Modeling complex data densities7. Regression models8. Classification modelsPart III. Connecting Local Models9. Graphical models10. Models for chains and trees11. Models for gridsPart IV. Preprocessing12. Image preprocessing and feature extractionPart V. Models for Geometry13. The pinhole camera14. Models for transformations15. Multiple camerasPart VI. Models for Vision16. Models for style and identity17. Temporal models18. Models for visual wordsPart VII. AppendicesA. OptimizationB. Linear algebraC. Algorithms.'Computer vision and machine learning have married and this book is their child. It gives the machine learning fundamentals you need to participate in current computer vision research. It's really a beautiful book, showing everything clearly and intuitively. I had lots of 'aha!' moments as I read through the book. This is an important book for computer vision researchers and students, and I look forward to teaching from it.' William T. Freeman, Massachusetts Institute of Technology Show more